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model update

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  1. README.md +9 -9
README.md CHANGED
@@ -2,7 +2,7 @@
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  datasets:
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  - relbert/semeval2012_relational_similarity
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  model-index:
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- - name: relbert/relbert-roberta-large-semeval2012-average-prompt-e-nce
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  results:
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  - task:
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  name: Relation Mapping
@@ -14,7 +14,7 @@ model-index:
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  metrics:
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  - name: Accuracy
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  type: accuracy
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- value: 0.9348214285714286
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  - task:
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  name: Analogy Questions (SAT full)
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  type: multiple-choice-qa
@@ -153,27 +153,27 @@ model-index:
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  value: 0.8953388906170653
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  ---
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- # relbert/relbert-roberta-large-semeval2012-average-prompt-e-nce
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  RelBERT fine-tuned from [roberta-large](https://huggingface.co/roberta-large) on
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  [relbert/semeval2012_relational_similarity](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity).
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  Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail).
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  It achieves the following results on the relation understanding tasks:
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- - Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/relbert-roberta-large-semeval2012-average-prompt-e-nce/raw/main/analogy.json)):
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  - Accuracy on SAT (full): 0.6016042780748663
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  - Accuracy on SAT: 0.6023738872403561
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  - Accuracy on BATS: 0.7476375764313508
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  - Accuracy on U2: 0.5482456140350878
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  - Accuracy on U4: 0.6111111111111112
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  - Accuracy on Google: 0.86
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- - Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-large-semeval2012-average-prompt-e-nce/raw/main/classification.json)):
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  - Micro F1 score on BLESS: 0.9193912912460449
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  - Micro F1 score on CogALexV: 0.8481220657276995
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  - Micro F1 score on EVALution: 0.685807150595883
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  - Micro F1 score on K&H+N: 0.962092230646171
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  - Micro F1 score on ROOT09: 0.898464431212786
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- - Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-large-semeval2012-average-prompt-e-nce/raw/main/relation_mapping.json)):
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- - Accuracy on Relation Mapping: 0.9348214285714286
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  ### Usage
@@ -184,7 +184,7 @@ pip install relbert
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  and activate model as below.
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  ```python
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  from relbert import RelBERT
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- model = RelBERT("relbert/relbert-roberta-large-semeval2012-average-prompt-e-nce")
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  vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, )
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  ```
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@@ -211,7 +211,7 @@ The following hyperparameters were used during training:
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  - n_sample: 640
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  - gradient_accumulation: 8
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- The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/relbert-roberta-large-semeval2012-average-prompt-e-nce/raw/main/trainer_config.json).
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  ### Reference
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  If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/).
 
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  datasets:
3
  - relbert/semeval2012_relational_similarity
4
  model-index:
5
+ - name: relbert/roberta-large-semeval2012-average-prompt-e-nce
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  results:
7
  - task:
8
  name: Relation Mapping
 
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  metrics:
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  - name: Accuracy
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  type: accuracy
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+ value: 0.8490079365079365
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  - task:
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  name: Analogy Questions (SAT full)
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  type: multiple-choice-qa
 
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  value: 0.8953388906170653
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  ---
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+ # relbert/roberta-large-semeval2012-average-prompt-e-nce
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  RelBERT fine-tuned from [roberta-large](https://huggingface.co/roberta-large) on
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  [relbert/semeval2012_relational_similarity](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity).
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  Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail).
161
  It achieves the following results on the relation understanding tasks:
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+ - Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/roberta-large-semeval2012-average-prompt-e-nce/raw/main/analogy.json)):
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  - Accuracy on SAT (full): 0.6016042780748663
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  - Accuracy on SAT: 0.6023738872403561
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  - Accuracy on BATS: 0.7476375764313508
166
  - Accuracy on U2: 0.5482456140350878
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  - Accuracy on U4: 0.6111111111111112
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  - Accuracy on Google: 0.86
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+ - Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/roberta-large-semeval2012-average-prompt-e-nce/raw/main/classification.json)):
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  - Micro F1 score on BLESS: 0.9193912912460449
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  - Micro F1 score on CogALexV: 0.8481220657276995
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  - Micro F1 score on EVALution: 0.685807150595883
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  - Micro F1 score on K&H+N: 0.962092230646171
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  - Micro F1 score on ROOT09: 0.898464431212786
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+ - Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/roberta-large-semeval2012-average-prompt-e-nce/raw/main/relation_mapping.json)):
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+ - Accuracy on Relation Mapping: 0.8490079365079365
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  ### Usage
 
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  and activate model as below.
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  ```python
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  from relbert import RelBERT
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+ model = RelBERT("relbert/roberta-large-semeval2012-average-prompt-e-nce")
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  vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, )
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  ```
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  - n_sample: 640
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  - gradient_accumulation: 8
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+ The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/roberta-large-semeval2012-average-prompt-e-nce/raw/main/trainer_config.json).
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  ### Reference
217
  If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/).